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Front-pinned spectral collocation for the flame problem and related ignition problems
arXiv cs.NA0 views
arXiv:2610.27864v1 Announce Type: new Abstract: We consider global Chebyshev collocation for the ignition problems $y'=y^q(1-y)$, $y(0)=\delta$, $q\ge2$; for $q=2$ this is the flame problem, a standard stiff test equation. After rescaling to $[-1,1]$ the solution has an interior layer of width $O(\mu)$, $\mu=(q-1)\delta^{q-1}$, at a $\delta$-dependent position. Two obstacles arise: plain Chebyshev collocation needs $O(1/\mu)$ nodes, and imposing the initial condition amplifies errors by a factor of order $1/\mu$, which makes the collocation equations severely ill-conditioned. Our main contribution addresses the second one. Since all solutions are translates of one profile, the initial condition can be replaced by an equivalent interior condition. For an unknown $u=\varphi(y)$, imposing it where the speed $|\varphi'f_q|$ of $u$ along the orbit is maximal bounds the propagator of the linearized equation by one; for $u=\ln y$ this is $y(x^\ast)=(q-1)/q$. For the first obstacle we use the sinh map of Tee and Trefethen: the time function is explicit, the complex singularities of the profile lie exactly at $\pm(2k+1)\pi i$, and the map parameters follow in closed form, so that $O(\ln(1/\mu))$ nodes suffice. In double precision the pinned scheme reaches the interpolation error of the exact solution in five or six Newton iterations for $\mu$ down to $10^{-12}$, whereas the initial-condition form fails or converges to wrong discrete solutions, also in an integral (Birkhoff) reformulation. Computations in $256$-bit arithmetic confirm the predicted rate down to errors of $10^{-70}$, AAA approximation locates the singularities to within $2\%$, and the method carries over to an Arrhenius reaction rate, with all parameters computed numerically.
Source:https://arxiv.org/abs/2610.27864PreviousA Novel Two-Level Domain Decomposition Method with Physically Motivated Coarse Space for Surface Acoustic Wave Device SimulationsNextInverse domain uncertainty quantification in hemodynamics: sampling virtual patient cohorts under constraints
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